Synthetic experiments reveal that class-dependent effects appear in both perturbation-based and ground-truth evaluations of time series feature attributions, often producing contradictory rankings of attribution quality due to differences in feature amplitude or temporal extent between classes.
In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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A data-centric survey of federated learning that ranks non-IID data traits by influence on convergence, links splitting protocols to real phenomena, and examines data-related defenses under clean and adversarial conditions.
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Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation
Synthetic experiments reveal that class-dependent effects appear in both perturbation-based and ground-truth evaluations of time series feature attributions, often producing contradictory rankings of attribution quality due to differences in feature amplitude or temporal extent between classes.